A Better Way to Predict the Indian Monsoon

A new study finds that including Himalayan topography and land-atmosphere interactions improves climate models.

Source:
Geophysical Research Letters

The Indian monsoon arrived early in 2013, leaving thousands of residents stranded by floods and landslides. Researchers are developing tools to better forecast the Indian monsoon, which is essential for sustainable water resources management through storage and efficient use. Credit: AFP PHOTO/INDIAN ARMY, CC BY 2.0

By
Emily Underwood 4 May 2018

Roughly 1.7 billion people rely on the annual Indian summer monsoons for water to drink, grow crops, and raise livestock. The deluges usually fall between June and September, delivering roughly 80% of the Indian subcontinent’s yearly rainfall. But sometimes the monsoons fail to deliver, resulting in drought, or dump too much water too fast, causing devastating floods. Accurately forecasting such vagaries can be lifesaving, but predictions often fall short. Now, a new model—including improved representation of land processes, along with mountainous Himalayan topography—promises greater accuracy.

One region where scientists have consistently failed to accurately predict monsoon behavior in the 21st century is over the sprawling, 860,000-square-kilometer Ganga river basin in central India. Each year, the June–September forecasts simulated by the operational model of the India Meteorological Department seem to have a “dry bias” over the Ganga basin, predicting less rain will fall than actually does.

To fix this bug, Devanand et al. decided to use their models to zoom in closer to Earth’s surface. The standard models for predicting Indian monsoons don’t take into account local topographical details, such as the western Himalaya. These models often miss complex interactions between the land and atmosphere, such as how moisture evaporates from the land, then falls back down as precipitation. The team remedied this by combining a regional climate model called the Weather Research and Forecasting Model with two land-surface models that can simulate interactions between the atmosphere and north central India’s agricultural land, along with Himalayan mountainous topography. To verify their model’s accuracy, they checked it against real-world weather data from 1981 to 2015.

Including this finer-grained detail largely did the trick, the authors report, correcting the dry bias of earlier models from a rainfall deficit of −4.82 millimeters per day to −1.37 millimeters per day. Global models, which smooth out local topography, allow too much cold, dry air to travel into the region, skewing predictions toward less rainfall, they concluded. Next, the team hopes to tackle another potentially important source of error in the models: irrigation. When used on a large scale, as it is in the Ganga basin, irrigation can lead to cooler local temperatures and more rain due to recycled precipitation. (Geophysical Research Letters, https://doi.org/10.1002/2018GL077218, 2018)

Eos is the leading source for trustworthy news and perspectives about the Earth and space sciences and their impact. Its namesake is Eos, the Greek goddess of the dawn, who represents the light shed on understanding our planet and its environment in space by the Earth and space sciences.